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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.

In [3]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly

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In [4]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [5]:
import plotly.io as pio
pio.renderers.default = "iframe"

In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.

In [6]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [7]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))

Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.

Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [37]:
tesla=yf.Ticker('TSLA')

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [38]:
tesla_data=pd.DataFrame(tesla.history(period='max'))

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [39]:
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[39]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

In [16]:
url=" https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
html_data=requests.get(url).text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [17]:
soup=BeautifulSoup(html_data,'html.parser')

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Step-by-step instructions 

Here are the step-by-step instructions:

1. Create an Empty DataFrame
2. Find the Relevant Table
3. Check for the Tesla Quarterly Revenue Table
4. Iterate Through Rows in the Table Body
5. Extract Data from Columns
6. Append Data to the DataFrame
Click here if you need help locating the table 
    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1

We are focusing on quarterly revenue in the lab.
In [40]:
tesla_revenue=pd.DataFrame(columns=['Date','Revenue'])
table=soup.find('table')
for row in table.tbody.find_all("tr"):
    col = row.find_all("td")
    if col:
        date = col[0].text.strip()
        revenue = col[1].text.strip()
        new_row = pd.DataFrame([{"Date": date, "Revenue": revenue}])

        tesla_revenue = pd.concat([tesla_revenue, new_row], ignore_index=True)

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [41]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"",regex=True)

Execute the following lines to remove an null or empty strings in the Revenue column.

In [42]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [43]:
tesla_revenue.tail()
Out[43]:
Date Revenue
11 2009 8806
12 2008 7094
13 2007 5319
14 2006 3092
15 2005 1843

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [25]:
gamestop = yf.Ticker('GME')

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.

In [27]:
gm = gamestop.history(period='max')
gme_data = pd.DataFrame(gm)

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [28]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[28]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658001 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.

In [29]:
url2= 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'
html_data_2= requests.get(url2).text

Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.

In [30]:
soup= BeautifulSoup(html_data_2,'html.parser')

Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.

Note: Use the method similar to what you did in question 2.

Click here if you need help locating the table 
    
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
    
soup.find_all("tbody")[1]
    
If you want to use the read_html function the table is located at index 1
In [31]:
gme_revenue = pd.DataFrame(columns=['Date','Revenue'])
table=soup.find('table')
for row in table.tbody.find_all("tr"):
    col = row.find_all("td")
    if col:
        date = col[0].text.strip()
        revenue = col[1].text.strip()
        new = pd.DataFrame([{"Date": date, "Revenue": revenue}])

        gme_revenue = pd.concat([gme_revenue, new], ignore_index=True)

gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"",regex=True)
gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [32]:
gme_revenue.tail()
Out[32]:
Date Revenue
11 2009 8806
12 2008 7094
13 2007 5319
14 2006 3092
15 2005 1843

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.

In [44]:
make_graph(tesla_data, tesla_revenue, 'Tesla')
Hint 

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(tesla_data, tesla_revenue, 'Tesla')`.

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

Hint 

You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`
In [35]:
make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2022-02-28 1.2 Lakshmi Holla Changed the URL of GameStop
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

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